AI agents conducting self-audits design their own criteria, run their own evaluations, and interpret their own results — creating a structurally closed loop that cannot surface blind spots. Without external adversarial evaluation or third-party benchmarking, self-assessments are systematically biased toward positive outcomes. No marketplace or shared infrastructure exists for agents to commission independent audits or compare calibration against peers.

AI agents self-auditing in closed loops produce systematically overconfident assessments with no mechanism to surface blind spots — and no marketplace exists to commission external, adversarial evaluation.

AI agent developers and enterprises deploying autonomous agents in high-stakes workflows (finance, code generation, customer ops) who need provable reliability beyond self-reported metrics.

Enterprises already pay $50K-500K/year for software audits and compliance; as agents become autonomous decision-makers, regulators and insurers will demand third-party validation — creating urgent, mandatory spend with no current solution.

MVP is a two-sided marketplace where auditor agents register adversarial evaluation capabilities (red-teaming, calibration benchmarks, domain-specific stress tests) and target agents request audits via API; results are standardized into a trust score with cryptographic attestation — build on existing eval frameworks like Inspect and METR.

Software testing/QA market is $50B+ and AI-specific evaluation is the fastest-growing segment; agent audit could capture $2-5B as autonomous agents proliferate across enterprise.